{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21972,"status":"ok","timestamp":1690071346878,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"},"user_tz":420},"id":"scKll44AuD1v","outputId":"b6d717c5-e342-4b71-a303-d77a530a25f7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting transformers==4.28.0\n","  Downloading transformers-4.28.0-py3-none-any.whl (7.0 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m26.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0) (3.12.2)\n","Collecting huggingface-hub<1.0,>=0.11.0 (from transformers==4.28.0)\n","  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n","\u001b[2K     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xxhash-3.2.0\n","Collecting accelerate\n","  Downloading accelerate-0.21.0-py3-none-any.whl (244 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.22.4)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.1)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n","Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.0.1+cu118)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) 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80.3MB/s]\n"]}],"source":["!gdown 1a1v06PvGaWmTCWvL33o2C133D90SRzMC # download cw_texts_clean_bert.csv\n","!mkdir output\n","!mkdir /data\n","!mv cw_texts_clean_bert.csv /data"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["4abee4443fc44383a53448f94b7f0ee1","5b54ebf5a23a4e038404d1751b1d15a3","dfcc04664e0e45898e1477e323fa167b","0eaecf27069846468a62fa4d8dbec55c","8aac40ee629e4834a6ab79c0bdebc1b1","2cb24f8e4d2d477faaa95c2813919b2b","570dbeda589e4ea7ba049455ac85abd1","c77ee79d248a4a828201ac50e588a590","70485460232d4a36bd23774e41dce08a","b156fd923d074d719bd313d03cf90682","f2725ccc080d4463a161a0fc1c6be83f","adf8e949190747a4ba059516ea6dd81c","b7d05c4912b747ccb5eda65def82cb00","ffba4c360f5c4d45a3a9ded5381d6d43","b02bec1e578a431aad67380d45b47bef","e2ce6bcfb7414335ae55788cc7d34439","3aeb53415050490abd7827d7cf8caa5c","89b442aac48e46aea4d0073473c770b3","88c6c562df644e9c8d9b68eea10652c2","835d2858a3e84e2db1a9a394219cb0e1","76259f98dca54f4b88e61cdc8209aee5","d03d6686c1a04e9d8c86295557c4140c","67e37e2151ea42f2a62a95f7297ecfc6","b76e5e6098ea40278c38426fa29118b3","4f0ed8b1b07440deb4af0a370324bae8","01376b569a234f4db6afda31038b2cf6","0398c3c47d0f44058f79ddeef73868bb","4b7356d22339441dafbdf3b2c946d96f","a4cfa7bccb4a4472aba54602cd77db6b","f8974c77f2ab48f998276b51f6291afb","a23b75f18fa84f9c9f577f604552ab80","2b88ea96abca4dd6a08f38ad2e316bb7","c42c125fcf54444d9652d4aaaf87bbf5","db8dcbd5f89a488e9e0184c52bb755de","f7ee45174de84c078ec95c6ab0eb6bba","bbbb83112d3943a19c3602b16614f776","91f2930a5f7d4a0a9fe991fd798e1693","e8e078afe417441bb3614df5e42cb131","9701436ce041483e88f2e028c5fef954","30f44a958edb4a589d563279b6b571e9","7b962936c7df4c34b1a393ef4ce3dfe7","00badb7b9eb44dbdbfa3634e854e557a","99355f64d7c840afa92ad278629449d6","245204f03a7e4fae8b5d2af1d18aea33","902c33b6edcf4e13a86a2c989f1a0d50","ee9fc85ca4c442538b5028f589cf2fb7","10449c55b5b94a7ca1877b0448b05401","00989c933b1f482d8bc836b2bdb724e3","d348e0c2b47c4798a78c442911d06345","b5b18aadb5a046508277360abc3f3775","a97c0dc598ab451ba6967c0f34449d97","47293defb52d4d40ad096f8d782400f1","5bfd92cf5daa497cbebe251b9f4f06c4","24947c45e3084be59e3274c30c54a0d7","c5986610ecb74dd1915244204ef3dbcc","a8a0295e8c7a4cffa39c40793d0e92ed","8db3bb74756f4fb0a7eea4f62e5eddcb","33ba7f418d65436b8a0840d06997b896","ba027dc110b74f2f994c1f8ac2c5a6e2","5f8273e08dff4cbba4dfc0b57a6878d5","fdd52cc76ef441bfbfa89420a3e07d7a","304f62be5dad47cc9cd2d004e23f2d39","e4f2b579e7134d3fb8f474cb0904594c","4df9a2463ac24964919c87d93a6e48c0","ea09b753bf9c42cc8b66d4b6a5f1937e","6d20f22b677c423b9b18ad016416c8a9","7034dfa672b6487eaa2d14974cb8e8bd","c03c990dd72a4370885c07a0d612b216","87b16d1c1957457e807a7c693faf8785","78ac4cdc302e47439d399c2939605307","3e1acb2c315d4a618796df1d90d7c729","8fa33a0f606d4fa79e8d6d20f64c5e88","3410fc629de44f93a19e82f060e736ee","1aff8b2a2aaf46aeafafa36f87b3281c","1540e4f7d7a04c36930baa8e6386873a","5a8658e31f22461e9dc20d5fef3b3c43","8f0f2426cd0f4f42b75e69f974dec102","422f90c060054a1d8ed65b27eddb38f5","89ecea1815764ec58f5bc344f16907f8","fb4074e4240f42429e894bb78552924d","183b7f3aaf03463183f802051de25e78","94534e2dc8084c3c9dec4540444b74ec","f91b8aa9294a4cb6840bcd583f43bbc5","1114f84d3667438982a00dd505c16890","a065b60aa7004055a84c4dd70d5f1a3b","a21039ddfc8b43e0ace2a7aeeba2d404","1f53455552354bfbb7dc4d7e2082f642","5cf6312001974246a41b13b6f27afe8e","64702bcdbdc8469ab18b439b809dc4d6","b85a5df3d3b54fcdb8e2b12b69d7190e","a31588fc655445c19712417ea4736d63","244bb7ceab064055bc7a00673ad046af","a08374e7b94a424595f8bdf99dd2a5bd","829814a2c8a14005bf02e26423e021e8","22d10702a83847b3a72ea2630f344130","0a65e54110cd48a09b786f765fa12c41","8dac6a0dc8f34341951faca19fbc012e","68eb7f099f5d495e887d24c42286290a","642029d655194f238e6bb6b3f09dd1d8","b3780f30a4174813bdf8269dc55302b7","1c871040cbb243279fea98dd5337d8be","1012dcfc14a1456a962432ed9741763f","d7ae449e5e7846789bb3a3bf784d3fb3","ee861a2ef06b465f9f7eceef1af1c22f","19481fbee50e4331ba717176f49bcf54","73265e55b61b43c285ebf8886f314867","441a75ecbd2648c58be45b8b712a2771","6758997766b4450a874fd158c327266e","cb9b5e3f80b64db7ad0a3a31d5442dcc","ffbce03a917e414bae0436c6815a4305"]},"id":"_UUtlbRAySQ_","outputId":"64b4a09d-cab1-470b-acb4-fa2a7290263b"},"outputs":[{"name":"stderr","output_type":"stream","text":["<ipython-input-3-ce742fad9a13>:26: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'] = ''\n","<ipython-input-3-ce742fad9a13>:27: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['year'] = train['year'].astype(int)\n","<ipython-input-3-ce742fad9a13>:28: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['month'] = train['month'].astype(int)\n"]},{"name":"stdout","output_type":"stream","text":["23868\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 0/22572 [00:00<?, ?it/s]<ipython-input-3-ce742fad9a13>:34: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 0\n","<ipython-input-3-ce742fad9a13>:32: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 1\n","100%|██████████| 22572/22572 [00:07<00:00, 3218.62it/s]\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4abee4443fc44383a53448f94b7f0ee1","version_major":2,"version_minor":0},"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/270 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"adf8e949190747a4ba059516ea6dd81c","version_major":2,"version_minor":0},"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"67e37e2151ea42f2a62a95f7297ecfc6","version_major":2,"version_minor":0},"text/plain":["Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/224k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"db8dcbd5f89a488e9e0184c52bb755de","version_major":2,"version_minor":0},"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/454k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"902c33b6edcf4e13a86a2c989f1a0d50","version_major":2,"version_minor":0},"text/plain":["Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a8a0295e8c7a4cffa39c40793d0e92ed","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/21924 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7034dfa672b6487eaa2d14974cb8e8bd","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/648 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"422f90c060054a1d8ed65b27eddb38f5","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/1296 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"64702bcdbdc8469ab18b439b809dc4d6","version_major":2,"version_minor":0},"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/437M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["Some weights of the model checkpoint at snowood1/ConfliBERT-scr-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['bert.pooler.dense.weight', 'classifier.weight', 'classifier.bias', 'bert.pooler.dense.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","Some weights of the model checkpoint at snowood1/ConfliBERT-scr-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['bert.pooler.dense.weight', 'classifier.weight', 'classifier.bias', 'bert.pooler.dense.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]},{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='13710' max='13710' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [13710/13710 45:17, Epoch 10/10]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Mse</th>\n","      <th>Mae</th>\n","      <th>R2</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>500</td>\n","      <td>4.240400</td>\n","      <td>3.990163</td>\n","      <td>3.990163</td>\n","      <td>1.718373</td>\n","      <td>-0.018681</td>\n","      <td>0.075617</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>3.690200</td>\n","      <td>3.932526</td>\n","      <td>3.932526</td>\n","      <td>1.572282</td>\n","      <td>-0.003966</td>\n","      <td>0.047840</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>3.657600</td>\n","      <td>5.167010</td>\n","      <td>5.167010</td>\n","      <td>1.260829</td>\n","      <td>-0.319127</td>\n","      <td>0.649691</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>3.749000</td>\n","      <td>4.638227</td>\n","      <td>4.638227</td>\n","      <td>1.340601</td>\n","      <td>-0.184130</td>\n","      <td>0.668210</td>\n","    </tr>\n","    <tr>\n","      <td>2500</td>\n","      <td>3.872400</td>\n","      <td>5.109526</td>\n","      <td>5.109526</td>\n","      <td>1.268536</td>\n","      <td>-0.304452</td>\n","      <td>0.649691</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>3.714400</td>\n","      <td>3.917002</td>\n","      <td>3.917002</td>\n","      <td>1.618185</td>\n","      <td>-0.000003</td>\n","      <td>0.052469</td>\n","    </tr>\n","    <tr>\n","      <td>3500</td>\n","      <td>3.772700</td>\n","      <td>4.505708</td>\n","      <td>4.505707</td>\n","      <td>1.365143</td>\n","      <td>-0.150298</td>\n","      <td>0.668210</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>3.653100</td>\n","      <td>3.922789</td>\n","      <td>3.922789</td>\n","      <td>1.589512</td>\n","      <td>-0.001480</td>\n","      <td>0.066358</td>\n","    </tr>\n","    <tr>\n","      <td>4500</td>\n","      <td>3.594900</td>\n","      <td>3.981901</td>\n","      <td>3.981901</td>\n","      <td>1.712160</td>\n","      <td>-0.016571</td>\n","      <td>0.075617</td>\n","    </tr>\n","    <tr>\n","      <td>5000</td>\n","      <td>3.739800</td>\n","      <td>4.672468</td>\n","      <td>4.672468</td>\n","      <td>1.334635</td>\n","      <td>-0.192872</td>\n","      <td>0.668210</td>\n","    </tr>\n","    <tr>\n","      <td>5500</td>\n","      <td>3.630700</td>\n","      <td>3.944766</td>\n","      <td>3.944767</td>\n","      <td>1.677434</td>\n","      <td>-0.007091</td>\n","      <td>0.061728</td>\n","    </tr>\n","    <tr>\n","      <td>6000</td>\n","      <td>3.663600</td>\n","      <td>4.023801</td>\n","      <td>4.023801</td>\n","      <td>1.504173</td>\n","      <td>-0.027268</td>\n","      <td>0.046296</td>\n","    </tr>\n","    <tr>\n","      <td>6500</td>\n","      <td>3.692100</td>\n","      <td>4.881816</td>\n","      <td>4.881816</td>\n","      <td>1.301063</td>\n","      <td>-0.246318</td>\n","      <td>0.665123</td>\n","    </tr>\n","    <tr>\n","      <td>7000</td>\n","      <td>3.566300</td>\n","      <td>4.018476</td>\n","      <td>4.018476</td>\n","      <td>1.737361</td>\n","      <td>-0.025909</td>\n","      <td>0.075617</td>\n","    </tr>\n","    <tr>\n","      <td>7500</td>\n","      <td>3.677300</td>\n","      <td>4.390985</td>\n","      <td>4.390985</td>\n","      <td>1.388737</td>\n","      <td>-0.121010</td>\n","      <td>0.020062</td>\n","    </tr>\n","    <tr>\n","      <td>8000</td>\n","      <td>3.612400</td>\n","      <td>4.321750</td>\n","      <td>4.321750</td>\n","      <td>1.404384</td>\n","      <td>-0.103334</td>\n","      <td>0.020062</td>\n","    </tr>\n","    <tr>\n","      <td>8500</td>\n","      <td>3.741900</td>\n","      <td>3.979126</td>\n","      <td>3.979126</td>\n","      <td>1.709985</td>\n","      <td>-0.015863</td>\n","      <td>0.075617</td>\n","    </tr>\n","    <tr>\n","      <td>9000</td>\n","      <td>3.529400</td>\n","      <td>4.224457</td>\n","      <td>4.224457</td>\n","      <td>1.428847</td>\n","      <td>-0.078495</td>\n","      <td>0.032407</td>\n","    </tr>\n","    <tr>\n","      <td>9500</td>\n","      <td>3.571300</td>\n","      <td>3.974181</td>\n","      <td>3.974181</td>\n","      <td>1.533669</td>\n","      <td>-0.014600</td>\n","      <td>0.047840</td>\n","    </tr>\n","    <tr>\n","      <td>10000</td>\n","      <td>3.502200</td>\n","      <td>3.917898</td>\n","      <td>3.917898</td>\n","      <td>1.606113</td>\n","      <td>-0.000232</td>\n","      <td>0.052469</td>\n","    </tr>\n","    <tr>\n","      <td>10500</td>\n","      <td>3.552600</td>\n","      <td>4.014230</td>\n","      <td>4.014230</td>\n","      <td>1.509215</td>\n","      <td>-0.024825</td>\n","      <td>0.046296</td>\n","    </tr>\n","    <tr>\n","      <td>11000</td>\n","      <td>3.475100</td>\n","      <td>3.917014</td>\n","      <td>3.917014</td>\n","      <td>1.618696</td>\n","      <td>-0.000006</td>\n","      <td>0.052469</td>\n","    </tr>\n","    <tr>\n","      <td>11500</td>\n","      <td>3.391700</td>\n","      <td>3.920103</td>\n","      <td>3.920103</td>\n","      <td>1.596846</td>\n","      <td>-0.000794</td>\n","      <td>0.066358</td>\n","    </tr>\n","    <tr>\n","      <td>12000</td>\n","      <td>3.636400</td>\n","      <td>3.920935</td>\n","      <td>3.920934</td>\n","      <td>1.594312</td>\n","      <td>-0.001007</td>\n","      <td>0.066358</td>\n","    </tr>\n","    <tr>\n","      <td>12500</td>\n","      <td>3.553900</td>\n","      <td>4.068769</td>\n","      <td>4.068769</td>\n","      <td>1.483057</td>\n","      <td>-0.038749</td>\n","      <td>0.030864</td>\n","    </tr>\n","    <tr>\n","      <td>13000</td>\n","      <td>3.409900</td>\n","      <td>4.032024</td>\n","      <td>4.032024</td>\n","      <td>1.500020</td>\n","      <td>-0.029368</td>\n","      <td>0.046296</td>\n","    </tr>\n","    <tr>\n","      <td>13500</td>\n","      <td>3.632700</td>\n","      <td>3.969568</td>\n","      <td>3.969568</td>\n","      <td>1.536982</td>\n","      <td>-0.013423</td>\n","      <td>0.047840</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["{'loss': 4.2404, 'learning_rate': 0.0019270605397520059, 'epoch': 0.36, 'step': 500}\n","{'eval_loss': 3.9901628494262695, 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[1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]]\n","4.382559138605911\n","-0.0006161413563547935\n","Now whole corpus\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b3780f30a4174813bdf8269dc55302b7","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/23868 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Now predicting\n"]},{"data":{"text/html":[],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["[[1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]\n"," [1.2315078]]\n","DF saved\n"]},{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='1492' max='1492' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1492/1492 01:31]\n","    </div>\n","    "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["{'eval_loss': 3.5506680011749268,\n"," 'eval_mse': 3.5506680011749268,\n"," 'eval_mae': 1.562588095664978,\n"," 'eval_r2': -0.010088127676783909,\n"," 'eval_accuracy': 0.04135243841126194,\n"," 'eval_runtime': 91.6085,\n"," 'eval_samples_per_second': 260.543,\n"," 'eval_steps_per_second': 16.287,\n"," 'epoch': 10.0}"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["# the following lines (177 in total) are directly copied from Haffner et al. (2023), Political Analysis\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from datasets import Dataset\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","from sklearn.metrics import mean_absolute_error\n","from transformers import TrainingArguments, Trainer\n","import torch\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import mean_squared_error\n","from sklearn.metrics import r2_score\n","from numpy.random import seed\n","from tqdm import tqdm\n","seed(1)\n","\n","final = pd.read_csv('/data/cw_texts_clean_bert.csv')\n","\n","print(len(final))\n","final['fatalities_log'] = final['fatalities_log'].astype(float)\n","\n","train = final[final['year'] < 2021]\n","train.reset_index(inplace=True, drop=True)\n","test = final[final['year'] == 2021]\n","test.reset_index(inplace=True, drop=True)\n","\n","train['split_dummy'] = ''\n","train['year'] = train['year'].astype(int)\n","train['month'] = train['month'].astype(int)\n","\n","for i in tqdm(range(len(train))):\n","    if train['year'][i] == 2020 and train['month'][i] > 6:\n","        train['split_dummy'][i] = 1\n","    else:\n","        train['split_dummy'][i] = 0\n","\n","train['split_dummy'].value_counts()\n","\n","train1 = train[train['split_dummy'] == 0]\n","vali = train[train['split_dummy'] == 1]\n","\n","X_train = train1['final_text']\n","y_train = train1['fatalities_log']\n","X_vali = vali['final_text']\n","y_vali = vali['fatalities_log']\n","X_test = test['final_text']\n","y_test = test['fatalities_log']\n","X_train = pd.DataFrame(X_train)\n","X_test = pd.DataFrame(X_test)\n","X_vali = pd.DataFrame(X_vali)\n","y_vali = pd.DataFrame(y_vali)\n","y_train = pd.DataFrame(y_train)\n","y_test = pd.DataFrame(y_test)\n","\n","df_train = pd.concat([X_train, y_train], axis=1)\n","df_val = pd.concat([X_vali, y_vali], axis=1)\n","df_test = pd.concat([X_test, y_test], axis=1)\n","\n","\n","raw_train_ds = Dataset.from_pandas(df_train)\n","raw_val_ds = Dataset.from_pandas(df_val)\n","raw_test_ds = Dataset.from_pandas(df_test)\n","\n","BASE_MODEL = \"snowood1/ConfliBERT-scr-uncased\"\n","LEARNING_RATE = 2e-3\n","MAX_LENGTH = 256\n","BATCH_SIZE = 16\n","EPOCHS = 10 # reduced from 20\n","\n","tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)\n","def model_init():\n","    model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL, num_labels=1, ignore_mismatched_sizes=True)\n","    # for param in model.bert.parameters():\n","    #     param.requires_grad = False\n","    return model\n","\n","\n","ds = {\"train\": raw_train_ds, \"validation\": raw_val_ds, \"test\": raw_test_ds}\n","\n","\n","def preprocess_function(examples):\n","    label = examples[\"fatalities_log\"]\n","    examples = tokenizer(examples[\"final_text\"], truncation=True, padding=\"max_length\", max_length=256)\n","\n","    # Change this to real number\n","    examples[\"label\"] = float(label)\n","    return examples\n","\n","\n","for split in ds:\n","    ds[split] = ds[split].map(preprocess_function)\n","\n","\n","def compute_metrics_for_regression(eval_pred):\n","    logits, labels = eval_pred\n","    labels = labels.reshape(-1, 1)\n","\n","    mse = mean_squared_error(labels, logits)\n","    mae = mean_absolute_error(labels, logits)\n","    r2 = r2_score(labels, logits)\n","    single_squared_errors = ((logits - labels).flatten() ** 2).tolist()\n","\n","    # Compute accuracy\n","    # Based on the fact that the rounded score = true score only if |single_squared_errors| < 0.5\n","    accuracy = sum([1 for e in single_squared_errors if e < 0.25]) / len(single_squared_errors)\n","\n","    return {\"mse\": mse, \"mae\": mae, \"r2\": r2, \"accuracy\": accuracy}\n","\n","training_args = TrainingArguments(\n","    output_dir=\"/data/output/conflibert\",\n","    learning_rate=LEARNING_RATE,\n","    per_device_train_batch_size=BATCH_SIZE,\n","    per_device_eval_batch_size=BATCH_SIZE,\n","    num_train_epochs=EPOCHS,\n","    evaluation_strategy=\"steps\",\n","    eval_steps=500,\n","    save_total_limit=10,\n","    load_best_model_at_end=True,\n","    weight_decay=0.01,\n","    seed=123\n",")\n","\n","class RegressionTrainer(Trainer):\n","    def compute_loss(self, model, inputs, return_outputs=False):\n","        labels = inputs.pop(\"labels\")\n","        outputs = model(**inputs)\n","        logits = outputs[0][:, 0]\n","        loss = torch.nn.functional.mse_loss(logits, labels)\n","        return (loss, outputs) if return_outputs else loss\n","\n","\n","trainer = RegressionTrainer(\n","    model_init=model_init,\n","    args=training_args,\n","    train_dataset=ds[\"train\"],\n","    eval_dataset=ds[\"validation\"],\n","    compute_metrics=compute_metrics_for_regression,\n",")\n","\n","trainer.train()\n","\n","for obj in trainer.state.log_history:\n","    print(obj)\n","\n","trainer.eval_dataset=ds[\"test\"]\n","trainer.evaluate()\n","\n","raw_pred_test, _, _ = trainer.predict(ds[\"test\"])\n","print(raw_pred_test[:20])\n","pd.DataFrame(raw_pred_test).to_csv('/data/cw_pred_test_zero.csv')\n","\n","df_results = pd.DataFrame(columns=['Test_MSE','Test_R2'], index=range(0,1))\n","mse = mean_squared_error(ds['test']['fatalities_log'], raw_pred_test)\n","r_squared = r2_score(ds['test']['fatalities_log'], raw_pred_test)\n","print(mse)\n","print(r_squared)\n","df_results['Test_MSE'][0] = mse\n","df_results['Test_R2'][0] = r_squared\n","df_results.to_csv('/data/bert_metrics.csv', index=False)\n","\n","## Whole CW\n","print('Now whole corpus')\n","raw_train1_ds = Dataset.from_pandas(final)\n","ds = {\"test\": raw_train1_ds}\n","\n","## Create dataset\n","\n","for split in ds:\n","    ds[split] = ds[split].map(preprocess_function)\n","\n","print('Now predicting')\n","raw_pred, _, _ = trainer.predict(ds[\"test\"])\n","print(raw_pred[:20])\n","pd.DataFrame(raw_pred).to_csv('/data/cw_bert_pred_zero.csv')\n","\n","print('DF 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